Cloud and machine learning in a new service is set to work together. Meet Azure ML, a new project that Microsoft will launch in July to its customers, for the creation of applications that can predict the future on the basis of previous data.

Azure ML is a cloud service that will allow scientists and developers to effectively integrate predictive analytics data into their applications, helping organizations to use huge amounts of data and to provide all benefits of cloud machine learning.

Companies have realized that data analysis is an effective means to improve their services and optimize their strategies. So far the data analysis was a rather complex process, causing many skills both commercial, as statistics, mathematics or technology. The financial cost of cleaning of the data and their interpretation had been often too costly and time consuming for companies to exploit their data. But with automation and platforms such as Microsoft Azure ML, big data analytics may be converted into gold mines.

Predict the Future with Big Data

Microsoft says Azure ML will bring together the capabilities of new analytics tools, powerful algorithms developed for Microsoft products like the Xbox and Bing, and years of machine learning experience into one simple and easy-to-use cloud service. For customers, this means virtually none of the startup costs associated with authoring, developing and scaling machine learning solutions.

By providing the computing power of their servers, Microsoft promises a strong reduction of costs and increasing efficiency of machine learning, which is already used in many sectors, from search engines to recognize spam from the suggestions of products online at prevention of fraud, from directions to the Cortana, the personal assistant on Windows Phone 8.1.

Azure ML has been designed to allow the creation of applications that involve the future, by analyzing the previous data. Companies will have the opportunity to exploit the computing power of Microsoft servers, greatly reducing the cost usually needed to implement a specific solution for their business. Additionally, Azure ML could save time and staff when making predictions, since for these tasks are traditionally required a large team of scientists to analyze the data and IT equipment to develop the application.

The machine learning is a branch of artificial intelligence that deals with the study of systems that can be learned from the data. The machine learning is, for example, used to automatically distinguish the real from spam messages. In the future, it will be used to reduce waiting times in the emergency room, to predict outbreaks and to prevent crimes.

Azure ML to Compete with Watson

The strategy behind Azure ML is different, but the goals are similar to those of Watson. The machine learning was democratized by IBM with Watson. IBM Watson was born with the ambitious goal of defining a new era in the field of cognitive technologies. The platform can be used in all those areas that have a point of contact with the concept of artificial intelligence, taking advantage of the interaction and integration with the potential offered by cloud computing.

Watson allows a company to process structured and unstructured data in order to be able to predict events such as fraud, failures, market opportunities, etc. Watson has already found practical application in the service of clinical research in oncology, but in a short time, is set to revolutionize a larger number of sectors – energy, health, retail, editorial, finance, biotech, pharmaceutical and others.

Microsoft’s goal can only be reached if the machine learning becomes accessible to everyone. For that, Azure ML design includes a study tool for business analysts, an API for deployment and an SDK for writing applications. Developers will use the popular open source R language to write applications. Currently, some selected partners are using a preliminary version of the service. A public preview will be available from July.

Azure ML could become a very lucrative service for Microsoft since the explosion analysis of data in the world is still in its infancy and future prospects are more than promising.